PADTHAI-MM: Principles-based Approach for Designing Trustworthy, Human-centered AI using MAST Methodology
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| Format: | Preprint |
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2024
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| _version_ | 1866913039141306368 |
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| author | Cohen, Myke C. Kim, Nayoung Ba, Yang Pan, Anna Bhatti, Shawaiz Salehi, Pouria Sung, James Blasch, Erik Mancenido, Michelle V. Chiou, Erin K. |
| author_facet | Cohen, Myke C. Kim, Nayoung Ba, Yang Pan, Anna Bhatti, Shawaiz Salehi, Pouria Sung, James Blasch, Erik Mancenido, Michelle V. Chiou, Erin K. |
| contents | Despite an extensive body of literature on trust in technology, designing trustworthy AI systems for high-stakes decision domains remains a significant challenge, further compounded by the lack of actionable design and evaluation tools. The Multisource AI Scorecard Table (MAST) was designed to bridge this gap by offering a systematic, tradecraft-centered approach to evaluating AI-enabled decision support systems. Expanding on MAST, we introduce an iterative design framework called \textit{Principles-based Approach for Designing Trustworthy, Human-centered AI using MAST Methodology} (PADTHAI-MM). We demonstrate this framework in our development of the Reporting Assistant for Defense and Intelligence Tasks (READIT), a research platform that leverages data visualizations and natural language processing-based text analysis, emulating an AI-enabled system supporting intelligence reporting work. To empirically assess the efficacy of MAST on trust in AI, we developed two distinct iterations of READIT for comparison: a High-MAST version, which incorporates AI contextual information and explanations, and a Low-MAST version, akin to a ``black box'' system. This iterative design process, guided by stakeholder feedback and contemporary AI architectures, culminated in a prototype that was evaluated through its use in an intelligence reporting task. We further discuss the potential benefits of employing the MAST-inspired design framework to address context-specific needs. We also explore the relationship between stakeholder evaluators' MAST ratings and three categories of information known to impact trust: \textit{process}, \textit{purpose}, and \textit{performance}. Overall, our study supports the practical benefits and theoretical validity for PADTHAI-MM as a viable method for designing trustable, context-specific AI systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_13850 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PADTHAI-MM: Principles-based Approach for Designing Trustworthy, Human-centered AI using MAST Methodology Cohen, Myke C. Kim, Nayoung Ba, Yang Pan, Anna Bhatti, Shawaiz Salehi, Pouria Sung, James Blasch, Erik Mancenido, Michelle V. Chiou, Erin K. Computers and Society Human-Computer Interaction Despite an extensive body of literature on trust in technology, designing trustworthy AI systems for high-stakes decision domains remains a significant challenge, further compounded by the lack of actionable design and evaluation tools. The Multisource AI Scorecard Table (MAST) was designed to bridge this gap by offering a systematic, tradecraft-centered approach to evaluating AI-enabled decision support systems. Expanding on MAST, we introduce an iterative design framework called \textit{Principles-based Approach for Designing Trustworthy, Human-centered AI using MAST Methodology} (PADTHAI-MM). We demonstrate this framework in our development of the Reporting Assistant for Defense and Intelligence Tasks (READIT), a research platform that leverages data visualizations and natural language processing-based text analysis, emulating an AI-enabled system supporting intelligence reporting work. To empirically assess the efficacy of MAST on trust in AI, we developed two distinct iterations of READIT for comparison: a High-MAST version, which incorporates AI contextual information and explanations, and a Low-MAST version, akin to a ``black box'' system. This iterative design process, guided by stakeholder feedback and contemporary AI architectures, culminated in a prototype that was evaluated through its use in an intelligence reporting task. We further discuss the potential benefits of employing the MAST-inspired design framework to address context-specific needs. We also explore the relationship between stakeholder evaluators' MAST ratings and three categories of information known to impact trust: \textit{process}, \textit{purpose}, and \textit{performance}. Overall, our study supports the practical benefits and theoretical validity for PADTHAI-MM as a viable method for designing trustable, context-specific AI systems. |
| title | PADTHAI-MM: Principles-based Approach for Designing Trustworthy, Human-centered AI using MAST Methodology |
| topic | Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2401.13850 |